Distinct and Shared Neural Basis Underlying Music and Language:A Perspective from Meta-analysis
Notice bibliographique
Résumé
Music and language are arguably the most characteristic traits of human beings. On one hand, previous functional magnetic resonance imaging(fMRI) studies have identified multiple cortical regions that are involved in the processing of both music and language, suggesting shared neural substrate for music and language. On the other hand, neuropsychological studies on brain-lesioned patients show the double dissociation between music and language, suggesting distinct neural substrates for music and language respectively. Here we used meta-analysis to examine the relation of the neural basis underlying music and language. First, we adopted the data from three meta-analysis studies on music and language respectively. Specifically, for the neural substrate of music, we focused on two processing levels specific to music processing, which were interval analysis(15 contrasts and 63 peaks) and structure analysis(19 contrasts and 217 peaks)(Lai, Xu, Song, Liu, 2013). For the neural substrate of language, three processing levels specific to language processing were selected, which were phonological analysis(86 contrasts and 344 peaks), lexico-semantic analysis(111contrasts and 339 peaks), and sentence analysis(65 contrasts and 218 peaks)(Vigneau et al., 2006; Vigneau et al., 2011). Second, we projected these peak activation elicited by processing either music or language onto the MNI(Montreal Neurological Institute) space to visualize the distribution of cortical regions involved in music and language with Caret. Finally, to explore the relation of neural substrates underlying music and language, we calculated whether pairs of peak activation were spatially overlapped or dissociated by K-means clustering and multivariate analysis of variance(MANOVA). The overlapping rate was estimated to quantify the extent to which music and language shared common neural substrates. We finally found 11 pairs of overlapping clusters. Music and language shared neural substrates at all levels of processing tested. Specially, the overlapped clusters from phonological processing of language and music perception were mainly in the auditory-motor loop, the overlapped clusters from semantic processing and music perception were in core loop, and the overlapped clusters of sentence processing and music were in cognition-emotion loop. In addition, the neural substrate underlying interval analysis of music overlapped with that underlying language processing as much as 50% in left hemisphere, which was mainly in the left superior temporal gyrus, left precentral gyrus, left pars triangularis of the inferior frontal gyrus and right insular. The neural substrate underlying structure analysis of music overlapped with that underlying language processing as much as 7% and 14% in the left and right hemisphere respectively, which was mainly in left Rolandic operculum, right pars opercularis of the inferior frontal gyrus and right insular. In sum, our study illuminates the functionality of the distinct and shared neural substrates underlying music and language. That is, for lower-level processes, such as interval analysis, phonological analysis and lexico-semantic analysis, music and language are more likely to share the same neural substrate in auditory analysis, auditory-motor integration and working memory. By contrast, for higher-level processes, especially in the structure analysis and sentence analysis, the neural substrate underlying music is more likely distinct from that underlying language. Models were proposed to illustrate the distinct and shared neural mechanisms underlying music and language, which invites future studies on the relation between music and language.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».